predicting new technology adoption from past technology usage behavior: the case of mobile channel...

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PREDICTING NEW TECHNOLOGY ADOPTION FROM PAST TECHNOLOGY USAGE BEHAVIOR: THE CASE OF MOBILE CHANNEL

ADOPTION

Last Revised: February 12, 2014 _____________________________________________________________________________________

ABSTRACT We predict mobile channel adoption by e-marketplace users based on their actual browsing and purchasing behaviors in the e-marketplace before the addition of the mobile channel. Our analysis, based on a large dataset from one of the leading e-marketplaces in South Korea, reveals that access and search behaviors before mobile channel introduction could be important predictors of mobile channel adoption. Specifically, order time dispersion (a behavioral proxy for need for anytime access) is negatively associated with the time it takes to adopt the mobile channel, whereas the proportion of orders based on keyword or category search, the mean number of product categories per order, and the mean display rank of orders (behavioral proxies for need for active search, broad search, and deep search, respectively) are positively associated with the time it takes to adopt the mobile channel. In addition to access and search behaviors, we considered privacy-related behaviors, transaction risk-related behaviors, assurance seeking behaviors, and preferences for transaction time, day, and product categories. Our findings shed new light on adoption research by demonstrating the efficacy of predicting a new technology adoption by individuals, based on their past technology usage behaviors. We also contribute to the emerging literature on mobile commerce by identifying significant predictors for mobile channel adoption. Finally, we suggest a simple scoring rule that can be used by firms to target customers who are most likely to adopt the mobile channel. KEYWORDS: Technology adoption, behavioral proxy, revealed preference, prediction, mobile channel, e-marketplace, access, search, survival analysis _____________________________________________________________________________________

1. INTRODUCTION

Since the seminal works of Davis (1985), Davis (1989), and Davis et al. (1989), who introduced

the technology acceptance model (TAM), one of the most prominent topics in Information

Systems during the past thirty years has been to identify and validate the antecedents of new

information technology adoption (or acceptance) by individuals or organizations. Specifically, IS

researchers have developed adoption theories by incorporating constructs, such as perceived ease

of use, perceived usefulness, subjective norm, and motivation into the model, and many

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subsequent studies validated the models with self-reported survey data across different contexts.

Research on the technology acceptance model and its variants such as TAM2 (Venkatesh and

Davis, 2000), UTAUT (Venkatesh et al., 2003), and UTAUT2 (Venkatesh et al., 2012) have

greatly advanced our knowledge in the domain.

Despite the popularity and frequent use of TAM and its variants, researchers have raised

concerns about the practicality of using such models. For example, Benbasat and Barki (2007)

state that “we need to identify the antecedents of the beliefs contained in adoption models in

order to benefit practice” (Benbasat and Barki, 2007, p. 215). Although prior studies have

extended TAM by including various factors (e.g., Taylor and Todd, 1995), they have not

improved the practicality of the model much, due to the increase in the complexity of the model.

Furthermore, firms having to decide whether or not to implement a new technology need to

know beforehand how users will perceive the new technology in terms of its ease of use and

usefulness. However, the extant adoption models are not efficient or effective in predicting users’

perceptions and adoption of the new technology, given that users differ in their perceptions about

a new technology. Moreover, identifying potential adopters requires collecting individual-

specific responses through costly and time-consuming surveys. Lastly, as Davis and Kottemann

(1994) point out, the antecedents of technology adoption should be measured beyond perceptions;

specifically, objective measures should be used, wherever possible, to improve the practicality of

a research model. In sum, there has been a call for research that advances technology adoption

research to the next stage (Bagozzi, 2007; Benbasat and Barki, 2007).

In this study, we aim to advance the current state of knowledge in technology adoption

research by predicting a new technology adoption, based on information extracted from an

individual’s past technology usage behaviors. Past behaviors are actual reflections of users’

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value systems, preferences, and habits; therefore, past usage behavior should contain rich and

valuable information that can predict users’ future behavior. This approach becomes feasible,

reliable and useful as firms accumulate a large amount of users’ detailed behavioral data within

their databases. Moreover, by using pre-existing behavioral data, firms can make predictions for

all customers in their database without conducting additional time-consuming surveys, which are

costly and can only cover a sample of customers.

We demonstrate this approach by investigating mobile channel adoption by users of an e-

marketplace as our research context, and we develop an empirical model to predict mobile

channel adoption based on users’ past e-commerce usage behaviors. Specifically, we use the past

browsing and purchasing behaviors of e-marketplace users through the PC channel to infer users’

latent needs related to access and search. Then, by applying the logic of fit between channel

capabilities and users’ latent needs, we relate these latent needs with the distinctive capabilities

of the current (i.e., PC) and new (i.e., mobile) channels to predict adoption of the new channel.

In addition to providing a context for demonstrating the technique for predicting new

technology adoption based on prior technology usage behavior, understanding the factors that

impact mobile channel adoption is valuable in itself, as the mobile channel has become a

significant conduit for electronic commerce. The mobile gross merchandise volume (GMV) of

eBay, for example, was expected to reach nearly $20 billion in revenue for 2013 (Barr, 2013).

Amazon also announced that mobile devices generated US $3-5 billion in sales, 5-8% of its net

sales in 2012 (Duryee, 2013). Therefore, it is not surprising that both practitioners and

academicians are interested in issues related to the mobile channel, and research in this domain is

gaining momentum (Gebauer et al., 2010; Ghose et al., 2012; Ghose and Han, 2011; Wu and

Wang, 2005). However, despite the increasing significance of the mobile channel, there is a

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paucity of empirical research employing large datasets to study users’ adoption behavior related

to the mobile channel. There have been a few studies on users’ behaviors in the mobile channel

using large-scale behavioral datasets, which have focused on comparing PC and mobile usage

behavior in terms of digital content creation and consumption (Ghose et al., 2012; Ghose and

Han, 2011), as well as their impact on revenue (Han et al., 2013). Our interest is different from

the above studies, in that we examine e-marketplace users’ search and purchase behaviors before

mobile channel introduction to predict their mobile channel adoption decision.

The mobile channel is of particular interest to this study for two additional reasons. First,

given that most firms offering e-commerce through the mobile channel already offer the PC

channel, information on past behaviors (i.e., usage of the PC channel), which is required by our

empirical technique to predict future behaviors (i.e., mobile channel adoption), is readily

available. Second, since PC and mobile channels are similar in terms of most features (such as

the same product assortment, inability to touch and feel products, and user login credentials), we

can concentrate on a few distinct features of the mobile channel, primarily access capability and

search capability, to construct a simple model that can be used to predict mobile channel

adoption.

To summarize, our study contributes not only to the stream of adoption literature by

demonstrating a novel approach of linking users’ technology adoption with their past technology

usage behaviors, but also to the growing body of research on m-commerce by identifying the

factors affecting e-commerce users’ mobile channel adoption.

The rest of this paper is organized as follows. In the following section, we identify the

distinctive aspects of the PC channel (prior technology) and the mobile channel (new

technology), focusing on the unique technological features and capabilities of the mobile channel.

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Then, we establish users’ latent needs that may be fulfilled by the distinct features and

capabilities of the mobile channel. We develop behavioral proxies that can capture the

underlying latent needs related to access and search. Employing the proxies and other control

factors that might affect adoption, we build a model, empirically test it, and demonstrate the

predictive power of the model. In addition, we suggest a simple scoring rule that can be used by

firms to target customers who are most likely to adopt the mobile channel. Finally, the paper

concludes with a discussion of the contributions, limitations, and suggestions for future research.

2. CONCEPTUAL BACKGROUND

2.1. Distinct Channel Capabilities of PC versus Mobile Channels

The notion of channel capability (Avery et al., 2012) refers to the enabling characteristics of a

channel that allows consumers to accomplish their shopping goals. Prior research indicates that

the channel capabilities of PC and mobile channels are distinct. Specifically, researchers suggest

that PC and mobile channels differ in terms of two primary capabilities - ubiquity and usability

(Clarke, 2001; Lee and Benbasat, 2003; Venkatesh and Ramesh, 2006; Zhang, 2007).

Compared to a PC, a mobile device is within easy reach of a user throughout the day, due

to its size and portability. Further, given that most of these mobile devices are Internet enabled,

such mobile devices allow users to connect to Internet applications such as e-marketplaces,

regardless of users’ physical locations. Therefore, the mobile channel provides a higher level of

ubiquitous access to such applications vis-à-vis a PC (Bang et al., 2013), which we refer to as the

“ubiquitous access capability” of the mobile channel. However, this benefit of ubiquity comes at

the cost of lower usability.

The small screens of mobile devices and related user interface constraints hamper users’

interactions with these devices, and thus lead to lower levels of usability. Although recent

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technological advances in mobile devices have addressed some of the usability issues raised in

prior studies (e.g., innovations such as touch screens, specially designed mobile applications,

simple login features, etc.), the mobile channel is still limited in terms of information search-

related usability vis-à-vis the PC channel (Bang et al., 2013). Therefore, we focus on the

information search aspect of usability and refer to it as the “limited information search capability”

of the mobile channel.

In summary, the mobile channel offers ubiquitous access capability, but limited

information search capability, whereas the PC channel offers extensive information search

capability, but constrained access capability. The main technological features of the two

channels and their associated capabilities are presented in Table 1.

----------------------------------------- Insert Table 1 about here

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2.2. Users’ Latent Needs and Fit with the Capabilities of the Mobile Channel

In light of the distinct channel capabilities of mobile and PC channels, we conjecture that PC

channel users would find the mobile channel more attractive; thus, they would be early adopters

(or more likely to adopt the channel) if they have a stronger latent need for more ubiquitous

access to the e-marketplace. On the other hand, PC channel users with a stronger latent need for

extensive search capabilities would be relatively slower in adopting (or less likely to adopt) the

mobile channel. Next, we further investigate access and search needs.

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2.2.1. Access Needs and Fit with the Mobile Channel

E-marketplace users are heterogeneous in terms of their needs for accessing the e-marketplace.

For example, some users prefer to access the e-marketplace only during specific times, whereas

others may want to access it anytime during a given day. Specifically, we identify two

dimensions of access needs—need for frequent access and need for anytime access.

Need for frequent access: We define the need for frequent access in terms of the number

of times a user would like to access the e-marketplace during a given time period. A user who

has a greater need for frequent access would also have the desire to access the e-marketplace

more frequently. Due to the ubiquitous access capability offered by the mobile channel through

the use of specialized applications, users having stronger needs for frequent access would find a

better fit with the mobile channel. Therefore, ceteris paribus, such users would be more likely to

adopt the mobile channel to satisfy their latent needs.

Need for anytime access: We define the need for anytime access as a user's desire for

ubiquitous access to the e-marketplace. For example, a user might want to access the e-

marketplace on her way to work, followed by the unexpected or unplanned need to purchase a

product. Such users with greater needs for anytime access would be able to gain more from the

ubiquitous access capability of the mobile channel, which allows flexible access by making the

time and location of access irrelevant. Thus, their likelihood of mobile channel adoption will be

higher.

2.2.2. Search Needs and Fit with the Mobile Channel

E-marketplace users continue to search for a better product until the marginal cost of search

exceeds the marginal benefit (Hoque and Lohse, 1999). Due to the limited information search

capability (i.e., the marginal search cost in a mobile device is larger than that in a PC),

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consumers who have the tendency to conduct intensive searches would face significant search

costs on mobile devices. We characterize e-marketplace users’ search needs in terms of their

propensity for active, broad, and deep search.

Need for active search: We define the need for active search as the propensity to engage

in directed search where users have the prior intention to search for a target product. For example,

users could search for a product by typing in keywords if they have a specific product or brand in

mind. On the other hand, there is a segment of users who access an e-marketplace without any

prior intention to purchase a specific product; they purchase products, primarily on impulse, after

being exposed to ads displayed on the e-marketplace. While the former segment of users is

characterized by active, more interactive search (which is associated with planned purchases),

the latter segment is characterized by passive, less interactive search (which is associated with

impulse purchases).

Since current mobile devices fit better with passive search behavior (e.g., touching

displayed ads on a screen) than with active search (e.g., typing in keywords) (Lee and Benbasat,

2003), e-marketplace users who tend to engage in passive search might feel less burdened in

using the mobile channel. On the other hand, due to the limited information search capability of

the mobile channel, e-marketplace users who tend to engage in active search might find the

mobile channel difficult to use. Therefore, e-marketplace users with an active search propensity

would be less likely to adopt the mobile channel, compared to those who exhibit a passive search

propensity.

Need for broad search: We define the need for broad search as a user’s propensity to

search for various product categories before placing an order during a single visit to an e-

marketplace. E-marketplace users having strong needs for broad search might be regarded as big

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basket shoppers, such as weekend shoppers at Costco, who tend to purchase a variety of things

during a single shopping trip. Due to the limited information search capabilities of the mobile

channel, they would face difficulty in conducting extensive search on the mobile channel.

Therefore, users having greater needs for broad search are likely to experience a lower fit with

the mobile channel, and are thus less likely to adopt it.

Need for deep search: We define the need for deep search as a user’s propensity to search

for various alternatives within a single product category before placing an order while visiting an

e-marketplace. E-marketplace users having strong needs for deep search might be regarded as

deliberate decision-makers who consider various alternatives when purchasing a product. Similar

to the other search dimensions, due to the limited information search capabilities of the mobile

channel, such users would find it difficult to engage in their normal search behavior on the

mobile channel than those who tend to consider only a few alternatives before placing an order.

Therefore, users having greater needs for deep search will experience a lower fit with the mobile

channel, and thus will be less likely to adopt it.

2.3. Developing Behavioral Proxies: Inferring Users’ Latent Needs from Past Behaviors

Because past human behaviors are actual reflections of value systems, preferences and habits, we

can infer valuable information on latent needs and preferences from past behaviors (Bamberg et

al., 2003; Vinson et al., 1977). In addition, this revealed preference approach can be more robust

against social desirability biases or memory errors, compared to stated preference measurements

such as surveys or in-person interviews (Borus, 1966; Fisher, 1993). Finally, behavioral proxies

can be easily created for the entire customer base; thus, this approach would be more scalable

and economical, compared to a survey-based method, which relies on sampling. In this

subsection, we develop behavioral proxies representing the latent needs of e-marketplace users

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from their access and search behaviors on the PC channel prior to mobile channel introduction.

We propose and empirically show that these behavioral proxies can be good predictors of mobile

channel adoption. Because our goal is to predict users’ mobile channel adoption, based on their

behavior on the PC channel, these behavioral proxies are taken from the period prior to mobile

channel introduction.

Need for frequent access: We measured the need for frequent access as the average

number of orders per day from the day that a user made the first order in the dataset to the day of

mobile channel introduction. We define an order as the act of putting a product into a basket, and

clicking “confirm.” Because a user needs to access an e-marketplace before placing an order, it is

reasonable to assume that order frequencies and access frequencies are highly correlated.1

Therefore, daily order frequency can be a good behavioral proxy for the need for frequent access.

Need for anytime access: We measured the need for anytime access as the dispersion in

time at which orders are placed, before mobile channel introduction. To explain this measure, let

us assume that a user has placed twenty-four orders within a given time period (say a month). On

one extreme, a user may place all of these twenty-four orders around the same time (say 7:00pm),

though on different days (refer to User #2 in Figure 1). Such a user with low order time

dispersion has a low need for anytime access, as her access behavior is concentrated within a

specific time period during which she is likely to be at a specific place. On the other extreme, a

user may place a total of twenty-four orders in a month at different time periods such that no two

orders are placed at the same time of day (refer to User #1 in Figure 1). Such a user with a larger

order time dispersion can be considered to have a greater need for anytime access; therefore,

such a user will a good fit with the ubiquitous mobile channel. As a result, order time dispersion

can be a good behavioral proxy for the need for anytime access. 1 Note that users’ access records are not available in our datasets.

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----------------------------------------- Insert Figure 1 about here

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We calculated order time dispersion with a revised inverse Simpson index, the inverse of

the sum of pair-wise multiplications of shares of order hours, weighted by the inverse of the hour

gap between pairs. The measure is constructed as follows:

𝑇𝐷𝑖 =𝑁

∑ ∑ 𝑠𝑖ℎ𝑚23ℎ𝑛=0 𝑠𝑖ℎ𝑛/(‖ℎ𝑚 − ℎ𝑛‖ + 1)23

ℎ𝑚=0

where ℎ𝑚(ℎ𝑛) is the hour of day, ‖ℎ𝑚 − ℎ𝑛‖ is the hour gap between ℎ𝑚 and ℎ𝑛, e.g., ‖ℎ𝑚 −

ℎ𝑛‖ = 2 if ℎ𝑚=17h, ℎ𝑛=19h or ℎ𝑚=1h, ℎ𝑛=23h, 𝑠𝑖ℎ𝑚(𝑠𝑖ℎ𝑛) represents the fraction of individual

i’s number of orders at ℎ𝑚(ℎ𝑛), and N is a normalizing constant (=0.2201).

The revised index not only captures categorical diversity over hours, such as families of

the Rényi entropy measure, but also takes into account interval diversity at the same time. Figure

1 describes how order time dispersion is calculated.

Need for active search: To measure the need for active search, we focused on actual

search behaviors before placing an order. There are three ways to search for products online: 1)

clicking on display ads; 2) typing in keywords; and 3) browsing categories. Clicking on displays

is less active and much easier than typing in keywords or browsing categories. Further, clicking

on displays may be associated with unplanned purchases, since a user does not know what would

be displayed before visiting the e-marketplace. Following the definition of active search

propensity, we measure it as the proportion of orders that are the result of active search (i.e.,

typing in keywords or browsing categories).

Need for broad search: We measure the need for broad search as the average number of

product categories per order before mobile channel introduction. Since a user needs to search for

products before placing an order, it is reasonable to assume that the number of different product

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categories per order and the number of searches for different product categories are highly

correlated. Therefore, the average number of different product categories per order can be a good

behavioral proxy for need for broad search.

Need for deep search: The need for deep search is measured as the average display rank

of ordered products. Display rank denotes the display location of an ordered product. If an

ordered product, for example, is listed at the top of the first search result page, then the display

rank of the product is one, whereas if the product is listed at the bottom or on the next search

result page, then the rank will be higher. The average display rank can be a good behavioral

proxy for need for deep search, since e-marketplace users having strong needs for deep search

tend to review products listed not only at the top of the first page, but also at the bottom of the

first page or on the following pages.

It is important to note here that a fundamental assumption underlying the prediction of

users’ future behaviors based on their past behaviors is that users’ behavioral patterns and

inferred needs should be stable, before and after mobile channel introduction. In other words, if

needs were dramatically affected or changed by mobile channel introduction, predicting future

behaviors based on inferred needs before mobile channel introduction would not be possible.

Therefore, as a robustness check, we checked the consistency of access and search behaviors,

before and after mobile channel introduction. These tests confirmed the consistency of access

and search behaviors.

2.4. Dependent and Control Variables

We measured Time to adopt, our dependent variable, as the number of days elapsed from the date

of mobile channel introduction (June 1, 2010) to the date of mobile channel adoption by each

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user.2 We define the date of mobile channel adoption as the date a user completes his/her first

transaction with mobile devices (smartphones, feature phones, and tablets) through mobile

networks (e.g., Wi-Fi, 3G, 4G networks). This definition allows us to specify Time to adopt in a

clear and objective manner.

To obtain more robust results, in addition to the aforementioned behavioral proxies for

access and search needs, we included the following control variables that might affect mobile

channel adoption. Including a number of control variables helps us not only rule out alternative

explanations, but also alleviates potential biases from omitted variables.

Privacy concerns: The mobile channel allows an e-marketplace to collect additional

personal information about users, such as current location at the time of ordering, etc.

Furthermore, one might expect more unsolicited promotions or pushed ads from an e-

marketplace on the mobile channel. As a result, users with high privacy concerns might hesitate

to adopt the mobile channel. To control for this possibility, we included behavioral proxies for

users’ privacy concerns in the model. Several conceptualizations and dimensions are proposed

for measuring information privacy (e.g., Malhotra et al., 2004; Smith et al., 1996). We employed

two dimensions proposed by Hui and Png (2006)3— seclusion concerns and secrecy concerns—

which are most suitable for this context. Seclusion concerns represent a dimension of privacy

indicating “the right to be left alone” (Hui and Png, 2006). Therefore, e-marketplace users who

have high seclusion concerns would be less likely to adopt the mobile channel, if they expect to

receive more unsolicited promotions or pushed ads after adopting the m-commerce channel. We

employed two dummy variables, whether or not to allow e-mail or SMS promotions from the e-

2 The Cox PH model, which is employed in the study, takes into account those who do not have time to adopt (non-adopters) as the baseline hazard. 3 Our data do not allow us to control for autonomy concerns, which represent “freedom from observation” (Hui and Png, 2006).

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marketplace before mobile channel introduction, to capture seclusion concerns. Secrecy concerns

represent privacy concerns created by the possession of personal information by others (Hui and

Png, 2006). In the e-marketplace that we examine, users were asked an optional question about

product categories of interest, and some users chose to reveal their preferences. Further, the e-

marketplace asked for permission from users regarding information sharing with an affiliated

company. We used two dummy variables indicating whether or not a user provided personal

interest information to the e-marketplace, and whether or not a user allowed information sharing,

to capture secrecy concerns.

Risk attitude: The transitory nature of using the mobile Internet and the low usability of

mobile devices could hamper mobile users when collecting product information. Limited user

experiences in mobile commerce can aggravate problems of information gathering and

processing. As a result, purchase decisions in the mobile channel might involve greater

uncertainty than in the PC channel. More risk-averse consumers are less likely to purchase under

the same level of uncertainty (Castaño et al., 2008; Peracchio and Tybout, 1996). Therefore, we

control for risk attitudes of e-marketplace users.

We measure the risk attitudes of e-marketplace users from their revealed preferences for

secure transactions and product returns. Two dummy variables were selected to measure

tendencies for secure transactions: (1) whether or not a user requested order confirmations, either

through e-mail or SMS; and (2) whether or not a user used a safer login system. E-marketplace

users who requested confirmation e-mails or SMSes from the e-marketplace can be regarded as

more risk-averse, since they are seeking more secure transactions. The safer login system is a

new way to authenticate user identity designed to reduce the potential risk of identity theft. The

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use of a safer login system requires an additional add-in installation. Therefore, e-marketplace

users who adopted the safer login system could be regarded as more risk-averse.

Product returns by users are also considered. Purchasing in the mobile channel might

involve greater uncertainty; therefore, e-marketplace users who have more product returns might

be reluctant to adopt the mobile channel because they can be expected to have more returns when

purchasing through the mobile channel. To control for this possibility, we included the product-

return tendencies of e-marketplace users in the model. We measured the tendency as the ratio of

returns to the total number of transactions prior to mobile channel introduction.

Dependence on assurance: Many online vendors offer an assurance on price (“price-

matching guarantee”) or an assurance on quality (“minimum quality guarantee”) for users who

put much weight on value attributes. On the one hand, such assurances could reduce the amount

of information that needs to be processed before making a purchase decision. For example, when

a user chooses from different sellers offering the same product, he/she does not need to compare

prices, if there is a price-matching guarantee. Therefore, e-marketplace users who tend to rely on

assurances might be more likely to adopt the mobile channel, since these assurance-seeking

behaviors would reduce the information burden in the mobile channel. On the other hand,

assurance-seeking behaviors might represent the risk attitudes of e-commerce users, because the

assurances could alleviate users’ concerns about the uncertainty involved in the product quality

and price. In either case, we need to control for users’ tendencies toward assurances. Two

variables—the proportion of orders containing products with price-matching guarantees and the

proportion of orders containing products with minimum quality guarantees—were selected to

measure users’ dependency on assurances.

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Preference for transaction day and time: In order to account for the possibility that users’

propensity to adopt the mobile channel may vary, depending on which day of the week and

which time of the day they prefer to shop in the e-marketplace, we control for users’ preference

for transaction day of the week and time of the day. We measure users’ preferences for

transaction day and time as their transaction density on each weekday and hour before mobile

channel introduction.

Product category preference: Prior research suggests that products with certain

characteristics (i.e., high time criticality and low information intensity) have a better fit with the

mobile channel, and therefore are more likely to be transacted on the channel (Bang et al., 2013).

This implies that depending on the product categories a user typically purchases on the e-

marketplace, his/her propensity to adopt the mobile channel may vary. Thus, we control for users’

order distribution over product categories. Specifically, we count the number of orders in each

product category normalized by the total number of orders before mobile channel introduction.

Age and gender: In general, demographic variables are known to explain large variances of

outcome variables in many social science studies. We control for the age and gender of e-

marketplace users that are known to be correlated with IT adoption (Venkatesh et al., 2003).

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3. METHODS

3.1. Data

This study uses two large datasets from the database of a large e-marketplace4 in South Korea

that had initially provided the PC channel only and launched the mobile channel later. Each of

the two datasets contains almost all data fields in the original database, including customer

demographics, e-market use settings (e.g., secure login), order/cancellation/return history, and

product information search behaviors before placing orders. Additionally, the datasets cover the

periods before and after the mobile channel launch. The first dataset contains a random sample of

30,000 users who did not adopt the mobile channel until one year after the introduction of the

mobile channel (t2), and their entire 1,454,803 online orders during a period of more than two

years (March 2009-June 2011). The second dataset contains a random sample of 30,000 users

who adopted the mobile channel before t2 and their entire 1,179,159 online orders, and 106,189

mobile orders placed during the same period.

Figure 2 depicts the composition of our datasets. t0 is the beginning of the timespan of the

datasets, t1 is the time when the e-marketplace launched the mobile channel, and t2 is the end

time of the timespan of the datasets. Note that no mobile orders exist between t0 and t1, because

the mobile channel was not available at that time.


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We randomly resampled 20,000 users without replacement from each dataset, and

merged the two samples into a dataset of 40,000 users for the main analysis (the remaining

samples are used later to check the predictive validity of our model.). Business users (168 users)

4 The e-marketplace ranks third in terms of revenue obtained through the PC channel and first in revenue obtained through the mobile channel in South Korea.

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were excluded from the dataset, since they show significantly different shopping patterns from

individual consumers in terms of purchasing volume and frequency. We also excluded users who

completed fewer than four transactions before t1 (18,769 users) because their purchasing

behaviors could not be identified due to a lack of sufficient data. Note that because we derive our

behavioral proxies for latent needs and control variables from the data before mobile channel

introduction, we can effectively control for potential endogeneity. Our main sample contains a

total of 21,063 users and their transaction records. Table 2 shows a basic description of the main

sample.


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3.2. Empirical Approach

Our data consist of mobile channel adopters and non-adopters. Adopters vary in terms of

adoption time. To capture the varying adoption time and the existence of non-adopters at the

same time, we employed a survival analysis technique. Specifically, we adopted a Cox

proportional hazard (PH) model to test our predictions (Cox, 1972). Similar to other survival

analysis techniques, the Cox-proportional hazard model focuses on the time to event. While other

parametric hazard models assume a particular shape for the hazard function, such as Weibull or

log-logistic, the Cox PH model has the advantage of placing no restrictions on the shape of the

baseline hazard. The Cox PH model is also known as one of the most general and robust

regression models (Li et al., 2010).

We know the exact time of the mobile channel launch, and our data cover periods both

before and after the mobile channel launch. Our data are right-censored, in that we know the start

time (i.e., all users enter the study at the same time), but we cannot observe the end time (the

time of adoption) for those who adopted the mobile channel after the end of our data collection

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period, or those who did not adopt the mobile channel at all. None of our observations dropped

out or got lost during the data period. Also, our sample is independently censored data, since our

data period is independent of the event times. Furthermore, mobile channel adoption is a one-

time event, and all of our independent variables, including the control variables, are not time

varying. Thus, we can implement the simplest form of the Cox PH model without concerns about

left-censoring or time-varying variables. The Cox PH model is expressed as

λ(t;𝐙) = λ0(𝑡)𝑒𝛽𝒁

where λ(t; Z) is a hazard function, Z is a vector of the explanatory variables, and β is a vector of

the parameters to be estimated. Z may include continuous variables, discrete variables, and

possible interactions. Just like a standard linear regression, if we have a discrete explanatory

variable v1 with m levels, then we will need to include (m−1) dummy variables (U1, U2, … , Um-

1), such that Uj = 1 if v1 = j.

The Cox PH model is a product of two quantities. The first part, λ0(t), is the baseline

hazard function and captures the underlying hazard for subjects, with all explanatory variables

Z1, ... ,Zn being equal to 0. Note that the ratio of the hazard function and the baseline hazard

function, λ(t; Z)/λ0(t), does not depend on time, t.

20

4. RESULTS

Descriptive statistics and correlations are reported in Tables 3A and 3B. The parameter estimates

for a model that predicts the time to adopt the mobile channel are depicted in Table 4.5 A

likelihood-ratio test between the model containing only the control variables and the main model

confirms that five independent variables collectively increase the overall likelihood significantly

(χ2 (5) = 8595.07, Prob. > χ2 = 0.000). We also evaluated the predictive power of the model by

computing Harrell’s C concordance statistic, which measures the agreement of predictions with

the observed adoption order. Harrell’s C is estimated to be 0.817, indicating that the model

correctly identifies the order of adoption times for pairs of e-commerce users 81.7% of the time.

This suggests that our model has strong predictive power. To further demonstrate the predictive

power of our model, we perform out-of-sample prediction in Section 4.2.

4.1. Impacts of Behavioral Proxies and Control Variables on Mobile Channel Adoption

The effect of the average number of orders per day, which is a behavioral proxy for need for

frequent access, is not significant. This might be due to the channel usage inertia of consumers

(Ansari et al., 2008; Falk et al., 2007). As purchasing experiences accumulate, a channel choice

decision becomes routinized by the learning process, and the habit of using the same channel

might be established. This channel choice inertia could be dominant, especially at the trial stage

of channel choice, due to the complexity and goal uncertainty of the channel choice decision

(Valentini et al., 2011). Therefore, although e-marketplace users having a higher average daily

5 Before interpreting the results, the proportional-hazard assumption of the Cox PH model needs to be checked. Either the numerical or graphical method can be employed. A test based on Schoenfeld residuals shows that the null hypothesis of proportionality is rejected for some variables. It is known, however, that no data in practice hold the perfect proportionality, and even a small misalignment could result in the rejection of the null hypothesis when dealing with large data (Klein and Moeschberger, 2003). Therefore, we employed graphical methods for the variables, which seem to violate the assumption (see Appendix 1). Because the plot for active search propensity shows a slight downward trend over time, we re-estimated the main model, including the time-dependent variable, ln t, for active search, and obtained qualitatively similar results.

21

number of orders might have greater access needs, their heavy use of the PC channel could

create inertia and hinder their adoption of the mobile channel.

The effect of order time dispersion, a behavioral proxy for need for anytime access, is

positive and significant. This result confirms that e-marketplace users having a greater need for

anytime access adopt the mobile channel more quickly, since it serves their needs better than the

PC channel. The effects of three search-related behavioral proxies are negative and significant,

indicating that e-marketplace users with fewer needs for active, broad, and deep search tend to

adopt the mobile channel faster, which is consistent with our conjecture.

Figure 3 shows the estimated baseline survival function, along with survival curves for

users at a high (75th percentile) and a very high (90th percentile) position in the distribution of

each significant independent variable. The survival rates (i.e., the likelihood of staying as a non-

adopter) for e-marketplace users with higher order time dispersion before mobile channel

introduction drop faster than the baseline after the introduction (see Figure 3-a). On the contrary,

the survival rates for e-marketplace users with higher active search propensity, search breadth,

and search depth before mobile channel introduction drop more slowly than the baseline after the

introduction (see Figure 3-b, 3-c, and 3-d).

Among the control variables, three out of four behavioral proxies for privacy concerns are

significant with the expected signs. E-marketplace users who allow e-mail or SMS promotions

(who have fewer seclusion concerns), and who authorize personal information transfer to an

affiliated company (who have fewer secrecy concerns) tend to adopt the mobile channel faster.

This confirms our conjecture that users with higher privacy concerns will be less likely to adopt

the mobile channel.

Regarding the variables’ ability to capture users’ risk attitudes, the rate of product returns

22

or cancellations is not significant, but two other proxies—whether or not a user requested e-mail

or SMS confirmations for completed transactions and whether or not a user used a safer login

system—are positive and significant. This implies that more risk-averse e-marketplace users are

less likely to adopt the mobile channel, which is consistent with our conjecture.

Users’ dependence on assurance (measured by the proportion of orders with a minimum

quality guarantee and the proportion of orders with a price-matching guarantee) turned out to be

positively related to mobile channel adoption. This confirms our conjecture that assurances can

alleviate users’ information search load, thereby making the limited information search

capability of the mobile channel less of an obstacle for adoption.

Age is negatively correlated with the likelihood to adopt mobile commerce, indicating

that younger users are more likely to adopt the mobile channel. The coefficient on gender is

negative and significant, suggesting that male users are more likely to adopt the mobile channel.

Lastly, we find significant effects of users’ preference for transaction day, transaction

time, and product category. The significance regarding preferences for day, time, and product

categories jointly suggests that mobile channel adoption is influenced not only by user

characteristics, but also by the adoption contexts and tasks (Gebauer et al., 2010).

----------------------------------------- Insert Table 3A about here

----------------------------------------- -----------------------------------------

Insert Table 3B about here ----------------------------------------- -----------------------------------------

Insert Table 4 about here -----------------------------------------

23


-----------------------------------------

4.2. Additional Analyses

Out-of-Sample Prediction. In order to test the predictive power of our model, we predicted

mobile channel adoption in the supplementary sample using the hazard equation and coefficients

obtained from the main sample. We performed prediction based on a fully restricted equation

(i.e., applying the same coefficients estimated from the main sample to the prediction sample),

which is a much stricter way of checking predictive validity than the usual way of quantifying

the correlation coefficient between different sets of measurements obtained for the same sample,

such as checking the correlation between newly developed measures and actual behaviors (e.g.,

TAM constructs and system choice in Szajna (1994)) (Steckel and Vanhonacker, 1993; Straub et

al., 2004). The predicted hazard rate of each individual in the supplementary sample was

generated from the equation and coefficients estimated from the main sample. We then

calculated the actual adoption rate as of one year after mobile channel introduction for various

percentiles of the hazard rate.


-----------------------------------------

Prediction results in Table 5 suggest that the model obtained from the main sample has

strong out-of-sample predictive power when applied to the supplementary sample. Without the

model, the probability of any user adopting the mobile channel as of one year after the

introduction is the ratio of the number of adopters to the total number of users,

4,579/9,838=0.465. With the model, users with higher hazard rates can be expected to be more

likely to adopt the mobile channel. Out of 492 users between the 90th and 95th percentiles, for

24

example, 430 users actually adopted the mobile channel within one year after the channel

introduction, indicating an adoption rate of 87.4%, which is much higher than the overall

adoption rate in the supplementary sample, 46.5%. On the contrary, users with lower hazard rates

can be expected to be less likely to adopt the mobile channel. Out of 492 users between the 5th

and 10th percentiles, 412 users actually did not adopt the mobile channel, even one year after the

channel introduction, showing an adoption rate of 16.3%, which is much lower than the overall

adoption rate in the supplementary sample, 53.5%. This prediction exercise adds to the validity

of our model and confidence in our results.

Adoption Scoring Rules. Online retailers or marketplaces that have not yet launched the

mobile channel do not have consumer adoption data to calibrate the prediction model. In order to

provide practical implications to those firms, we develop adoption scoring rules with access and

search variables in our model. Similar heuristic approaches have been used in marketing,

especially for predicting the future purchase behaviors of consumers, or their responses to firm

promotions based on their recency-frequency-monetary (RFM) values (Birant, 2011).

First, we list e-marketplace users in the main sample in descending order of their need for

anytime access (AA), and in ascending order of need for active search (SA), need for broad search

(SB), and need for deep search (SD) (see Appendix 3A and Appendix 3B). Then, we construct an

adoption score of each user by equally weighting the quartiles of the aforementioned access and

search variables (i.e., the sum of the AA quartiles and the average of the SA, SB, and SD

quartiles). Finally, we check the correlation between adoption scores and actual adoptions as of

one year after mobile channel introduction. The correlation between adoption scores and actual

adoptions is 0.35 (p<0.001), meaning that users’ adoption scores, which are computed from their

25

access and search behaviors on the PC channel, can be a good predictor for their mobile channel

adoption.

Table 6 shows the adoption score quartiles and the corresponding number of actual

adopters and non-adopters. Among the top 25% of adoption scores, 69.18% adopted the mobile

channel up to one year after mobile channel introduction, which is much higher than the average

adoption rate of 43.85%. In contrast, among the bottom 25% of adoption scores, only 23.94%

adopted the mobile channel, which is much lower than the average adoption rate. We believe

that this heuristic can be a useful tool for e-commerce companies, helping them assess which

users will be more likely to adopt the mobile channel before deciding to launch one.


-----------------------------------------

5. DISCUSSION AND CONCLUSION

In this study, we proposed a new approach to explaining and predicting a new technology

adoption, based on prior technology usage behaviors. Specifically, we demonstrated how users’

behavioral patterns, related to access and search, and captured through their actual transactions

on a PC channel of an e-marketplace, could be used to predict their mobile channel adoption. By

identifying the behavioral proxies that represent users’ latent needs for the mobile commerce

channel, we could construct a model that has strong predictive ability. The approach of

predicting a new technology adoption based on objective behavioral measures would become

more feasible, reliable, and useful, as a massive quantity and variety of user data accumulate in

firms’ databases. We believe this empirical approach can complement and extend the current

adoption research by providing an alternative way of measuring the latent needs of users in an

objective and reliable manner.

26

Linking past technology usage behaviors with new technology adoption has several

advantages. First, information on past usage behaviors, which is required to predict future

technology adoption, is readily available in many contexts. Firms can transform such data stored

in their databases into business intelligence by identifying users’ latent needs and predicting the

adoption of a new technology. Second, this approach allows firms to engage in individual-level

targeting for expanding its adopter base. By estimating the propensity to adopt a new technology

for each of their current users, firms can first target the users who are most likely adopt, and can

build their adopter bases effectively. Third, rather than relying on perceptual data, which are

costly to collect and less reliable, drawing business implications from behavioral histories of

users can be done on a large scale in a relatively inexpensive and reliable manner.

Our empirical results on mobile channel adoption also contribute to the burgeoning

research area of mobile commerce. Despite the significance of the mobile channel and many

calls for research, studies on m-commerce are still scant. This study is among the first to examine

mobile channel adoption based on a large dataset. With two datasets of 60,000 e-marketplace

users and over 2.5 million transactions in their online and mobile channels, we empirically show

that m-commerce channel adoption is largely influenced by users’ latent needs for access and

search capabilities.

This study provides several managerial implications for online retailers, which consider

mobile commerce as a new avenue for future growth. The method and findings from this study

could increase retailers’ mobile customer base effectively in the early introduction stage by

focusing on the customer segment, which is more prone to adopt mobile commerce. Specifically,

our results highlight that order time dispersion (a behavioral proxy for need for anytime access),

the proportion of orders followed by keyword or category search, the average number of product

27

categories per order, and the average display rank of orders (behavioral proxies for needs for

active, broad, and deep search, respectively) could be good predictors for mobile channel

adoption. Finally, the adoption scoring rules we proposed can help firms identify and target

potential adopters before they actually launch the mobile channel by computing the access and

search variables.

Our study has a few limitations. First, some of the behavioral measures for access and

search needs are derived based on orders rather than access or search behaviors, due to data

availability. Although they are expected to be highly associated, the measures need to be refined

and validated by future studies. Second, our data do not allow us to consider multi-channel

usage behaviors. For example, e-marketplace users might search for products in the PC channel,

which has extensive information search capability, and then purchase the products in the mobile

channel, which has ubiquitous access capability. Although consumers are likely to purchase at

the moment when they have enough information about the product, consumers could exploit the

benefits of each channel (Han et al., 2013). Collecting data regarding multi-channel usage and

examining the phenomenon of hopping from one channel to another would be an interesting

avenue for future research.

To conclude, our study predicted mobile channel adoption by e-marketplace users, based

on their actual browsing and purchasing behaviors in the e-marketplace, before the addition of

the mobile channel. Our findings show that users’ latent needs concerning access and search,

revealed through their transactions on the PC channel prior to mobile channel introduction, could

be important predictors of their mobile channel adoption. Our conceptual framework, based on

the fit between channel capabilities and users’ latent needs, as well as the behavioral proxies we

developed, will inform future research in this area.

28

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Table 1. Distinct Channel Capabilities between PC and Mobile Channels

Technological Features Channel Capabilities

Mobile Channel

Ubiquity of the Internet enabled device/ High portability of devices (e.g., small screens, light weight mobile devices) / Readiness of mobile devices to access the Internet

Ubiquitous access capability

Low input usability of mobile devices (e.g., text input) Limited information search capability

PC Channel

Stationarity of the Internet / Low portability of devices Constrained access capability

High input and output usability (e.g., large screens, keyboards) Extensive information search capability

Table 2. Basic Description of the Main Sample

Total Orders 1,523,563 Before the mobile channel launch (3/01/2009-5/31/2010) Online Orders 646,641

After the mobile channel launch (6/01/2010-6/30/2011)

Online Orders 841,962

Mobile Orders 34,960 Total Users 21,063 Mobile channel Adopters 9,237

Non-adopters 11,826 Gender Female 11,702

Male 9,361 Age 15-20 978

21-25 3,759 26-30 6,163 31-35 5,235 36-40 2,728 41-45 1,199 46-50 489 51- 512

Average membership duration (std.) 1 900.01 (193.85) (Days) Average order prices (std.) 2 34.38 (74.16) (USD) Note: 1 Duration at t2, 2 USD 1 = KRW 1,071.6 as of Nov 11, 2013

32

Table 3A. Descriptive Statistics (N=21,063)

Variable Mean S.D. Min Max 1. AF .059 .129 .009 11.031 2. AA .589 .127 .220 .949 3. SA .684 .293 0 1 4. SB 1.529 .479 1 5.833 5. SD 8.476 4.589 1 88 6. PC_em .769 .422 0 1 7. PC_sm .446 .497 0 1 8. PC_itr .327 .469 0 1 9. PC_tra .842 .364 0 1 10. RT .119 .151 0 1 11. CM .898 .302 0 1 12. LG .988 .111 0 1 13. AS_ql .667 .197 0 1 14. AS_pr .673 .240 0 1 15. Age 31.229 7.783 15 95 16. Gdr .444 .497 0 1 Note: AF=Need for frequent access, AA=Need for anytime access, SA=Need for active search propensity, SB=Need for broad search, SD=Need for deep search depth, PC_em=whether to allow email promotions, PC_sm=whether to allow SMS promotions, PC_itr= whether to give personal information on interests, PC_tra=whether to authorize personal information transfer to other companies, RT=product return or cancellation rates, CM=whether to request e-mail or SMS confirmations for transactions, LG=use of a safer login system, AS_ql=ratio of orders with quality assurance, AS_pr=ratio of orders with a price-matching guarantee, Gdr=gender (0=male, 1=female)

33

Table 3B. Correlations (N=21,063)

Variable AF AA SA SB SD PC_em PC_sm PC_itr PC_tra RT CM LG AS_ql AS_pr Age

AA 0.17

SA 0.04 0.06

SB 0.04 0.04 0.60

SD -0.03 0.00 0.01 0.02

PC_em 0.03 0.04 0.06 0.04 -0.03

PC_sm 0.01 0.02 0.02 0.02 -0.02 0.35

PC_itr -0.03 0.04 0.10 0.07 0.00 -0.01 0.05

PC_tra -0.08 0.22 0.04 0.03 0.03 -0.01 -0.01 0.17

RT 0.02 -0.01 -0.12 -0.10 -0.05 0.00 0.02 -0.03 -0.05

CM -0.05 -0.02 0.05 0.04 -0.01 0.27 0.19 0.02 0.00 -0.01

LG -0.03 -0.03 -0.01 -0.01 0.00 -0.02 -0.03 -0.01 0.01 -0.01 0.02

AS_ql 0.06 0.04 -0.05 -0.05 -0.04 0.02 0.01 -0.07 -0.13 -0.11 -0.01 0.00

AS_pr 0.05 0.00 0.09 0.03 -0.15 0.04 0.06 -0.09 -0.15 0.06 0.01 -0.01 0.12

Age 0.03 0.01 0.01 0.02 0.13 0.01 0.03 0.01 0.04 -0.10 -0.06 0.00 0.01 -0.16

Gdr -0.01 0.01 -0.29 -0.16 0.09 -0.03 -0.05 -0.06 -0.02 -0.04 -0.04 -0.02 -0.04 -0.26 0.11

Note: AF=Need for frequent access, AA=Need for anytime access, SA=Need for active search, SB=Need for broad search, SD=Need for deep search, PC_em=whether to allow email promotions, PC_sm=whether to allow SMS promotions, PC_itr= whether to give personal information on interests, PC_tra=whether to authorize personal information transfer to other companies, RT=product return or cancellation rates, CM=whether to request e-mail or SMS confirmations for transactions, LG=use of a safer login system, AS_ql=ratio of orders with quality assurance, AS_pr=ratio of orders with a price- matching guarantee, Gdr=gender (0=male, 1=female)

34

Table 4. Results from the Hazard Rate Analysis (N=21,063)

Dependent Variable: Time to adopt the mobile channel

Number of observations 21,063

Number of adoptions 9,237

Model fit Log likelihood = -82731.959, Prob. > χ2 = 0.0000; Harrell’s C = 0.817

Independent Variables Coef. (Std. errors) Note Number of orders per day (AF : Need for Access Frequency) -0.062 (0.134)

Access behaviors Order time dispersion (AA : Need for Anytime Access) 1.157 (0.092)***

Proportion of orders searched either through keyword typing or category browsing (SA: Need for Active Search)

-1.247 (0.048)***

Search behaviors Mean number of product categories per order (SB: Need for Search Breadth) -2.762 (0.05)***

Mean display rank of orders (SD: Need for Search Depth) -0.013 (0.003)***

Control Variables Email promotion allowance (PC_em: seclusion concerns) 0.087 (0.028)**

Privacy-related behaviors

SMS promotion allowance (PC_sms: seclusion concerns) 0.088 (0.023)***

Whether to report personal interests (PC_itr: secrecy concerns) 0.027 (0.024)

Whether to authorize personal information transfer to affiliated companies (PC_tra: autonomy concerns) 0.28 (0.03)***

Product return or cancellation ratios (RT) 0.032 (0.066)

Transaction risk-related behaviors

Whether to request e-mail or SMS confirmations for transactions (CM) -0.213 (0.035)***

Whether to use a safer login system (LG) -0.217 (0.088)*

Ratio of orders with a minimum quality guarantee (AS_ql) 0.69 (0.06)*** Assurance-seeking

behaviors Ratio of orders with a price-matching guarantee (AS_pr) 2.363 (0.102)***

Age (years) -0.017 (0.002)*** Demographics

Gender (0=male, 1=female) -0.071 (0.03)*

Density of orders by day of the week (base: Saturday) χ2(6) = 150.87, Prob. > χ2 = 0.000

Density of orders by time of the day (base: 23h) χ2(23) = 448.92, Prob. > χ2 = 0.000

Density of orders by product category6 (base: Cellphone/Smartphone) χ2

(37) = 658.14, Prob. > χ2 = 0.000

Note: *p<0.05, **p<0.01, ***p<0.001; two-tailed tests. The coefficients on the density of orders by day of the week, time of the day, and product categories are not shown for expositional brevity.

6 A total of 38 product categories were considered in the analysis. The list of categories is provided in Appendix 2.

35

Table 5. Prediction Results on the Supplementary Sample

Hazard Rate Percentile Percentile <1 1-5 5-10 10-25 25-50 50-75 75-90 90-95 95-99 99< Total

No. of Adopters 21 74 80 258 806 1,320 1129 430 362 95 4,575

No. of Non- adopters 78 319 412 1,218 1,653 1,140 347 62 30 4 5,263

Total 99 393 492 1,476 2,459 2,460 1476 492 392 99 9,838

P(A) 0.212 0.188 0.163 0.175 0.328 0.537 0.765 0.874 0.923 0.960 0.465

Note: P(A)=the actual proportion of adopters as of one year after mobile channel introduction.

Table 6. Results of Adoption Scoring Rules

Adoption score quartiles 1 2 3 4 Total

Adopters 1,337 (23.94%)

2,399 (42.47%)

2,497 (45.50%)

3,004 (69.18%)

9,237 (43.85%)

Non-adopters 4,247 (76.06%)

3,250 (57.53%)

2,991 (54.50%)

1,338 (30.82%)

11,826 (56.15%)

Total 5,584 5,649 5,488 4,342 21,063

36

Figure 1. Order Time Dispersion of Four Users

Notes: This figure displays the distribution of four hypothetical users’ orders over twenty-four hours. Even though they have an identical total number of orders, their order time dispersions are different. User #1 has a more even distribution, and thus a higher order time dispersion index (TD1=1.00) than user #2 (TD2=0.22). User #3 has a more localized distribution, and thus a lower order time dispersion index (TD3=0.48) than user #4 (TD4=0.59).

37

Figure 2. Composition of the Datasets

38

Figure 3. Survival Functions

(a) Order dispersion (b) Active search propensity

(c) Search breadth (d) Search depth

0.2

.4.6

.81

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180Days after the mobile channel introduction

Very high order dispersion High order dispersionBaseline survival

0.2

.4.6

.81


Very high search activeness High search activenessBaseline survival

0.2

.4.6

.81


Very high needs for search breadthHigh needs for search breadthBaseline survival

0.2

.4.6

.81


Very high needs for search depthHigh needs for search depthBaseline survival

39

Appendix 1. Plots for Testing the Proportionality Assumption

Need for frequent access Need for active search

Need for broad search Ratio of orders with a price-matching guarantee

Age Gender

-50

050

100

150

200

scal

ed S

choe

nfel

d - A

Q

0 100 200 300 400Time

bandwidth = .8

Test of PH Assumption

-20

-10

010

scal

ed S

choe

nfel

d - S

A

0 100 200 300 400Time

bandwidth = .8


-10

010

2030

scal

ed S

choe

nfel

d - S

B

0 100 200 300 400Time

bandwidth = .8

Test of PH Assumption-8

0-6

0-4

0-2

00

20sc

aled

Sch

oenf

eld

- AS

_pr

0 100 200 300 400Time

bandwidth = .8


-.50

.51

1.5

scal

ed S

choe

nfel

d - A

ge

0 100 200 300 400Time

bandwidth = .8


-10

-50

510

scal

ed S

choe

nfel

d - S

ex

0 100 200 300 400Time

bandwidth = .8


40

Appendix 2. The List of Thirty-eight Product Categories Used in the Analysis

1. Cellphones/Smartphones 2. Digital Cameras/DSLR Cameras 3. MP3/PMP/Electronic Dictionaries 4. Digital Goods (Software, Games) 5. PC-related Peripherals 6. TVs/Fridges/Washing Machines 7. e-Coupons/Gift Cards 8. Furniture 9. Bags/Wallets/Fashion Accessories 10. Health/Silver Products 11. Golf Clubs/Supplies 12. Men’s Fashions/Apparel/Underwear 13. GPS/Black Boxes 14. Laptops/Desktop PCs 15. Books/Music/DVDs 16. Hiking/Outdoor/Camping/Fishing 17. Stationery 18. Baby Products 19. Senior Clothing 20. Daily Supplies 21. Skin Care/Cosmetics 22. Sports Equipment 23. Watches/Jewelry/Fashion Accessories 24. Fresh Goods (Agricultural Products, Marine Products) 25. Flowers 26. Women’s Fashions/Apparel/Underwear 27. Shoes 28. Travels/Hotels/Airline Tickets 29. Children’s Apparel 30. Sound/Speaker 31. Automobile (Tires, Parts) 32. Toys/Dolls 33. Kitchen Appliances/Supplies 34. Confectionaries/Processed Foods 35. Childbirth/Maternity Dress 36. Bedding/Curtains/Carpets 37. Shopping Abroad 38. Perfume/Hair/Body Products

41

Appendix 3A. An Example Dataset: Past Access and Search Behaviors

User ID AA SA SB SD 1 0.95 0.44 2.17 6.25 2 0.22 0.14 1.00 4.00 3 0.25 1.00 1.50 13.89 4 0.76 0.36 2.08 9.75 5 0.84 0.35 1.83 5.00 6 0.29 0.24 1.37 9.33 7 0.54 0.92 1.50 11.65 8 0.57 0.19 2.07 15.20 9 0.67 1.00 1.80 16.25 10 0.81 0.74 1.15 2.89 11 0.30 0.46 1.90 12.38 12 0.33 0.29 2.14 17.17

Appendix 3B. User Quartiles and Adoption Scores of Users

ID AA Q ID SA Q ID SB Q ID SD Q ID AS Q 1 5

10 4 9 8 7

12 11 6 3 2

0.95 0.84 0.81 0.76 0.67 0.57 0.54 0.33 0.30 0.29 0.25 0.22

4 4 4 3 3 3 2 2 2 1 1 1

2 8 6

12 5 4 1

11 10 7 9 3

0.14 0.19 0.24 0.29 0.35 0.36 0.44 0.46 0.74 0.92 1.00 1.00

4 4 4 3 3 3 2 2 2 1 1 1

2 10 6 3 7 9 5

11 8 4

12 1

1.00 1.15 1.37 1.50 1.50 1.80 1.83 1.90 2.07 2.08 2.14 2.17

4 4 4 3 3 3 2 2 2 1 1 1

10 2 5 1 6 4 7

11 3 8 9

12

2.89 4.00 5.00 6.25 9.33 9.75

11.65 12.38 13.89 15.20 16.25 17.17

4 4 4 3 3 3 2 2 2 1 1 1

1 2 3 4 5 6 7 8 9

10 11 12

6.00 5.00 3.00 5.33 7.00 4.67 4.00 5.33 4.67 7.33 4.00 3.67

4 3 1 3 4 2 2 3 2 4 2 1

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